ArticleJournal of pathology informatics2022
An accessible, efficient, and accurate natural language processing method for extracting diagnostic data from pathology reports.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- From electronic health records to real-world evidence: Savana Next-Generation Registry digital solution.ESMO real world data and digital oncology · 2026Article
- Development and implementation of a data parsing protocol for companion animal cancer data.Veterinary pathology · 2026Article
- Natural language processing models for patient-centered summaries of prostatectomy pathology reports.Scientific reports · 2026Article
- Large language model-based approach for low-cost, rapid and accurate automated extraction of predictive biomarker testing results from Dutch pathology reports.Virchows Archiv : an international journal of pathology · 2026Article
- Natural Language Processing Methods Automate Molecular Marker Extraction From Glioma Pathology Reports.Neurosurgery · 2026Article
- Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.Journal of Korean medical science · 2026Article
- Automated Extraction of Imaging and Pathology Data From Diverse Prostate Cancer Electronic Records.JCO clinical cancer informatics · 2025Article
- Development of a Natural Language Processing Model for Extracting Kidney Biopsy Pathology Diagnoses.Kidney medicine · 2025Article
- Using Generative AI to Extract Structured Information from Free Text Pathology Reports.Journal of medical systems · 2025Article
- Synoptic reporting by summarizing cancer pathology reports using large language models.npj health systems · 2025Article
- Article
- Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LABDigital healthArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Context: Analysis of diagnostic information in pathology reports for the purposes of clinical or translational research and quality assessment/control often requires manual data extraction, which can be laborious, time-consuming, and subject to mistakes. Objective: We sought to develop, employ, and evaluate a simple, dictionary- and rule-based natural language processing (NLP) algorithm for generating searchable information on various types of parameters from diverse surgical pathology reports. Design: Data were exported from the pathology laboratory information system (LIS) into extensible markup language (XML) documents, which were parsed by NLP-based Python code into desired data points and delivered to Excel spreadsheets. Accuracy and efficiency were compared to a manual data extraction method with concordance measured by Cohen's κ coefficient and corresponding Results: The automated method was highly concordant (90%-100%, Conclusions: A simple, flexible, and scaleable NLP-based platform can be used to correctly, safely, and quickly extract and deliver linked data from pathology reports into searchable spreadsheets for clinical and research purposes.
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Registered trials
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